Overview

Dataset statistics

Number of variables46
Number of observations12684
Missing cells0
Missing cells (%)0.0%
Duplicate rows74
Duplicate rows (%)0.6%
Total size in memory4.5 MiB
Average record size in memory368.0 B

Variable types

Categorical36
Numeric10

Alerts

Dataset has 74 (0.6%) duplicate rowsDuplicates
age_category_representative_numeric_encoding is highly overall correlated with age and 3 other fieldsHigh correlation
income_category_representative_numeric_encoding is highly overall correlated with occupation and 2 other fieldsHigh correlation
education_ordinal_integer_encoding is highly overall correlated with education and 1 other fieldsHigh correlation
income_ordinal_integer_encoding is highly overall correlated with occupation and 2 other fieldsHigh correlation
age_ordinal_integer_encoding is highly overall correlated with age and 3 other fieldsHigh correlation
Bar_venue_visit_frequency_yes_response_ordinal_integer_encoding is highly overall correlated with Bar and 6 other fieldsHigh correlation
CoffeeHouse_venue_visit_frequency_yes_response_ordinal_integer_encoding is highly overall correlated with CoffeeHouse and 5 other fieldsHigh correlation
CarryAway_venue_visit_frequency_yes_response_ordinal_integer_encoding is highly overall correlated with Bar and 5 other fieldsHigh correlation
RestaurantLessThan20_venue_visit_frequency_yes_response_ordinal_integer_encoding is highly overall correlated with CoffeeHouse and 9 other fieldsHigh correlation
Restaurant20To50_venue_visit_frequency_yes_response_ordinal_integer_encoding is highly overall correlated with Bar and 9 other fieldsHigh correlation
destination is highly overall correlated with passenger and 3 other fieldsHigh correlation
passenger is highly overall correlated with destination and 2 other fieldsHigh correlation
weather is highly overall correlated with temperature and 1 other fieldsHigh correlation
temperature is highly overall correlated with weather and 1 other fieldsHigh correlation
time is highly overall correlated with destination and 2 other fieldsHigh correlation
coupon_venue_type is highly overall correlated with coupon_venue_type_ordinal_integer_encodingHigh correlation
expiration is highly overall correlated with expiration_category_representative_numeric_encoding and 1 other fieldsHigh correlation
gender is highly overall correlated with gender_binary_encodingHigh correlation
age is highly overall correlated with has_children and 3 other fieldsHigh correlation
maritalStatus is highly overall correlated with occupationHigh correlation
has_children is highly overall correlated with passenger and 3 other fieldsHigh correlation
education is highly overall correlated with occupation and 1 other fieldsHigh correlation
income is highly overall correlated with occupation and 2 other fieldsHigh correlation
Bar is highly overall correlated with CarryAway and 6 other fieldsHigh correlation
CoffeeHouse is highly overall correlated with RestaurantLessThan20 and 5 other fieldsHigh correlation
CarryAway is highly overall correlated with Bar and 5 other fieldsHigh correlation
RestaurantLessThan20 is highly overall correlated with CoffeeHouse and 9 other fieldsHigh correlation
Restaurant20To50 is highly overall correlated with Bar and 9 other fieldsHigh correlation
direction_same_or_opposite is highly overall correlated with passengerHigh correlation
expiration_category_representative_numeric_encoding is highly overall correlated with expiration and 1 other fieldsHigh correlation
time_category_representative_numeric_encoding is highly overall correlated with destination and 2 other fieldsHigh correlation
gender_binary_encoding is highly overall correlated with genderHigh correlation
expiration_binary_encoding is highly overall correlated with expiration and 1 other fieldsHigh correlation
coupon_venue_type_ordinal_integer_encoding is highly overall correlated with coupon_venue_typeHigh correlation
time_ordinal_integer_encoding is highly overall correlated with destination and 2 other fieldsHigh correlation
temperature_ordinal_integer_encoding is highly overall correlated with weather and 1 other fieldsHigh correlation
Bar_venue_visit_frequency_no_response_indicator is highly overall correlated with Bar and 4 other fieldsHigh correlation
CoffeeHouse_venue_visit_frequency_no_response_indicator is highly overall correlated with CoffeeHouse and 1 other fieldsHigh correlation
CarryAway_venue_visit_frequency_no_response_indicator is highly overall correlated with CarryAway and 3 other fieldsHigh correlation
RestaurantLessThan20_venue_visit_frequency_no_response_indicator is highly overall correlated with RestaurantLessThan20 and 4 other fieldsHigh correlation
Restaurant20To50_venue_visit_frequency_no_response_indicator is highly overall correlated with Bar and 7 other fieldsHigh correlation
occupation is highly overall correlated with age and 8 other fieldsHigh correlation
Bar_venue_visit_frequency_yes_response_ordinal_integer_encoding has 349 (2.8%) zerosZeros
CoffeeHouse_venue_visit_frequency_yes_response_ordinal_integer_encoding has 1111 (8.8%) zerosZeros
CarryAway_venue_visit_frequency_yes_response_ordinal_integer_encoding has 1594 (12.6%) zerosZeros
RestaurantLessThan20_venue_visit_frequency_yes_response_ordinal_integer_encoding has 1285 (10.1%) zerosZeros
Restaurant20To50_venue_visit_frequency_yes_response_ordinal_integer_encoding has 264 (2.1%) zerosZeros

Reproduction

Analysis started2022-12-03 07:39:35.637046
Analysis finished2022-12-03 07:40:07.854599
Duration32.22 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

destination
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
No Urgent Place
6283 
Home
3237 
Work
3164 

Length

Max length15
Median length4
Mean length9.4488332
Min length4

Characters and Unicode

Total characters119849
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowWork
3rd rowHome
4th rowNo Urgent Place
5th rowNo Urgent Place

Common Values

ValueCountFrequency (%)
No Urgent Place 6283
49.5%
Home 3237
25.5%
Work 3164
24.9%

Length

2022-12-02T23:40:07.927063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:08.030789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no 6283
24.9%
urgent 6283
24.9%
place 6283
24.9%
home 3237
12.8%
work 3164
12.5%

Most occurring characters

ValueCountFrequency (%)
e 15803
13.2%
o 12684
10.6%
12566
 
10.5%
r 9447
 
7.9%
N 6283
 
5.2%
P 6283
 
5.2%
c 6283
 
5.2%
a 6283
 
5.2%
l 6283
 
5.2%
t 6283
 
5.2%
Other values (7) 31651
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82033
68.4%
Uppercase Letter 25250
 
21.1%
Space Separator 12566
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15803
19.3%
o 12684
15.5%
r 9447
11.5%
c 6283
 
7.7%
a 6283
 
7.7%
l 6283
 
7.7%
t 6283
 
7.7%
n 6283
 
7.7%
g 6283
 
7.7%
m 3237
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
N 6283
24.9%
P 6283
24.9%
U 6283
24.9%
H 3237
12.8%
W 3164
12.5%
Space Separator
ValueCountFrequency (%)
12566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107283
89.5%
Common 12566
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15803
14.7%
o 12684
11.8%
r 9447
 
8.8%
N 6283
 
5.9%
P 6283
 
5.9%
c 6283
 
5.9%
a 6283
 
5.9%
l 6283
 
5.9%
t 6283
 
5.9%
n 6283
 
5.9%
Other values (6) 25368
23.6%
Common
ValueCountFrequency (%)
12566
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15803
13.2%
o 12684
10.6%
12566
 
10.5%
r 9447
 
7.9%
N 6283
 
5.2%
P 6283
 
5.2%
c 6283
 
5.2%
a 6283
 
5.2%
l 6283
 
5.2%
t 6283
 
5.2%
Other values (7) 31651
26.4%

passenger
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
Alone
7305 
Friend(s)
3298 
Partner
1075 
Kid(s)
1006 

Length

Max length9
Median length5
Mean length6.2888679
Min length5

Characters and Unicode

Total characters79768
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlone
2nd rowAlone
3rd rowAlone
4th rowAlone
5th rowAlone

Common Values

ValueCountFrequency (%)
Alone 7305
57.6%
Friend(s) 3298
26.0%
Partner 1075
 
8.5%
Kid(s) 1006
 
7.9%

Length

2022-12-02T23:40:08.125539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:08.229259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
alone 7305
57.6%
friend(s 3298
26.0%
partner 1075
 
8.5%
kid(s 1006
 
7.9%

Most occurring characters

ValueCountFrequency (%)
n 11678
14.6%
e 11678
14.6%
A 7305
9.2%
l 7305
9.2%
o 7305
9.2%
r 5448
6.8%
i 4304
 
5.4%
d 4304
 
5.4%
( 4304
 
5.4%
s 4304
 
5.4%
Other values (6) 11833
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58476
73.3%
Uppercase Letter 12684
 
15.9%
Open Punctuation 4304
 
5.4%
Close Punctuation 4304
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 11678
20.0%
e 11678
20.0%
l 7305
12.5%
o 7305
12.5%
r 5448
9.3%
i 4304
 
7.4%
d 4304
 
7.4%
s 4304
 
7.4%
a 1075
 
1.8%
t 1075
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
A 7305
57.6%
F 3298
26.0%
P 1075
 
8.5%
K 1006
 
7.9%
Open Punctuation
ValueCountFrequency (%)
( 4304
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71160
89.2%
Common 8608
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 11678
16.4%
e 11678
16.4%
A 7305
10.3%
l 7305
10.3%
o 7305
10.3%
r 5448
7.7%
i 4304
 
6.0%
d 4304
 
6.0%
s 4304
 
6.0%
F 3298
 
4.6%
Other values (4) 4231
 
5.9%
Common
ValueCountFrequency (%)
( 4304
50.0%
) 4304
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 11678
14.6%
e 11678
14.6%
A 7305
9.2%
l 7305
9.2%
o 7305
9.2%
r 5448
6.8%
i 4304
 
5.4%
d 4304
 
5.4%
( 4304
 
5.4%
s 4304
 
5.4%
Other values (6) 11833
14.8%

weather
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
Sunny
10069 
Snowy
1405 
Rainy
1210 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters63420
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunny
2nd rowSunny
3rd rowSunny
4th rowSunny
5th rowSunny

Common Values

ValueCountFrequency (%)
Sunny 10069
79.4%
Snowy 1405
 
11.1%
Rainy 1210
 
9.5%

Length

2022-12-02T23:40:08.319019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:08.408778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sunny 10069
79.4%
snowy 1405
 
11.1%
rainy 1210
 
9.5%

Most occurring characters

ValueCountFrequency (%)
n 22753
35.9%
y 12684
20.0%
S 11474
18.1%
u 10069
15.9%
o 1405
 
2.2%
w 1405
 
2.2%
R 1210
 
1.9%
a 1210
 
1.9%
i 1210
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50736
80.0%
Uppercase Letter 12684
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 22753
44.8%
y 12684
25.0%
u 10069
19.8%
o 1405
 
2.8%
w 1405
 
2.8%
a 1210
 
2.4%
i 1210
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
S 11474
90.5%
R 1210
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 63420
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 22753
35.9%
y 12684
20.0%
S 11474
18.1%
u 10069
15.9%
o 1405
 
2.2%
w 1405
 
2.2%
R 1210
 
1.9%
a 1210
 
1.9%
i 1210
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 22753
35.9%
y 12684
20.0%
S 11474
18.1%
u 10069
15.9%
o 1405
 
2.2%
w 1405
 
2.2%
R 1210
 
1.9%
a 1210
 
1.9%
i 1210
 
1.9%

temperature
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
80
6528 
55
3840 
30
2316 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters25368
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row55
3rd row30
4th row80
5th row80

Common Values

ValueCountFrequency (%)
80 6528
51.5%
55 3840
30.3%
30 2316
 
18.3%

Length

2022-12-02T23:40:08.493551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:08.582316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
80 6528
51.5%
55 3840
30.3%
30 2316
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 8844
34.9%
5 7680
30.3%
8 6528
25.7%
3 2316
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25368
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8844
34.9%
5 7680
30.3%
8 6528
25.7%
3 2316
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8844
34.9%
5 7680
30.3%
8 6528
25.7%
3 2316
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8844
34.9%
5 7680
30.3%
8 6528
25.7%
3 2316
 
9.1%

time
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
6PM
3230 
7AM
3164 
10AM
2275 
2PM
2009 
10PM
2006 

Length

Max length4
Median length3
Mean length3.3375118
Min length3

Characters and Unicode

Total characters42333
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6PM
2nd row7AM
3rd row6PM
4th row10AM
5th row2PM

Common Values

ValueCountFrequency (%)
6PM 3230
25.5%
7AM 3164
24.9%
10AM 2275
17.9%
2PM 2009
15.8%
10PM 2006
15.8%

Length

2022-12-02T23:40:08.670079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:08.773803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
6pm 3230
25.5%
7am 3164
24.9%
10am 2275
17.9%
2pm 2009
15.8%
10pm 2006
15.8%

Most occurring characters

ValueCountFrequency (%)
M 12684
30.0%
P 7245
17.1%
A 5439
12.8%
1 4281
 
10.1%
0 4281
 
10.1%
6 3230
 
7.6%
7 3164
 
7.5%
2 2009
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25368
59.9%
Decimal Number 16965
40.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4281
25.2%
0 4281
25.2%
6 3230
19.0%
7 3164
18.7%
2 2009
11.8%
Uppercase Letter
ValueCountFrequency (%)
M 12684
50.0%
P 7245
28.6%
A 5439
21.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 25368
59.9%
Common 16965
40.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4281
25.2%
0 4281
25.2%
6 3230
19.0%
7 3164
18.7%
2 2009
11.8%
Latin
ValueCountFrequency (%)
M 12684
50.0%
P 7245
28.6%
A 5439
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 12684
30.0%
P 7245
17.1%
A 5439
12.8%
1 4281
 
10.1%
0 4281
 
10.1%
6 3230
 
7.6%
7 3164
 
7.5%
2 2009
 
4.7%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
Coffee House
3996 
Restaurant(<20)
2786 
Carry out & Take away
2393 
Bar
2017 
Restaurant(20-50)
1492 

Length

Max length21
Median length17
Mean length13.513876
Min length3

Characters and Unicode

Total characters171410
Distinct characters26
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarry out & Take away
2nd rowBar
3rd rowCarry out & Take away
4th rowBar
5th rowRestaurant(<20)

Common Values

ValueCountFrequency (%)
Coffee House 3996
31.5%
Restaurant(<20) 2786
22.0%
Carry out & Take away 2393
18.9%
Bar 2017
15.9%
Restaurant(20-50) 1492
 
11.8%

Length

2022-12-02T23:40:08.878520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:08.989221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
coffee 3996
15.2%
house 3996
15.2%
restaurant(<20 2786
10.6%
carry 2393
9.1%
out 2393
9.1%
2393
9.1%
take 2393
9.1%
away 2393
9.1%
bar 2017
7.7%
restaurant(20-50 1492
 
5.7%

Most occurring characters

ValueCountFrequency (%)
a 20145
 
11.8%
e 18659
 
10.9%
13568
 
7.9%
r 11081
 
6.5%
t 10949
 
6.4%
u 10667
 
6.2%
o 10385
 
6.1%
s 8274
 
4.8%
f 7992
 
4.7%
C 6389
 
3.7%
Other values (16) 53301
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112002
65.3%
Uppercase Letter 19073
 
11.1%
Space Separator 13568
 
7.9%
Decimal Number 11540
 
6.7%
Close Punctuation 4278
 
2.5%
Open Punctuation 4278
 
2.5%
Math Symbol 2786
 
1.6%
Other Punctuation 2393
 
1.4%
Dash Punctuation 1492
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20145
18.0%
e 18659
16.7%
r 11081
9.9%
t 10949
9.8%
u 10667
9.5%
o 10385
9.3%
s 8274
7.4%
f 7992
 
7.1%
y 4786
 
4.3%
n 4278
 
3.8%
Other values (2) 4786
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
C 6389
33.5%
R 4278
22.4%
H 3996
21.0%
T 2393
 
12.5%
B 2017
 
10.6%
Decimal Number
ValueCountFrequency (%)
0 5770
50.0%
2 4278
37.1%
5 1492
 
12.9%
Space Separator
ValueCountFrequency (%)
13568
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4278
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4278
100.0%
Math Symbol
ValueCountFrequency (%)
< 2786
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2393
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 131075
76.5%
Common 40335
 
23.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20145
15.4%
e 18659
14.2%
r 11081
8.5%
t 10949
8.4%
u 10667
8.1%
o 10385
7.9%
s 8274
 
6.3%
f 7992
 
6.1%
C 6389
 
4.9%
y 4786
 
3.7%
Other values (7) 21748
16.6%
Common
ValueCountFrequency (%)
13568
33.6%
0 5770
14.3%
2 4278
 
10.6%
) 4278
 
10.6%
( 4278
 
10.6%
< 2786
 
6.9%
& 2393
 
5.9%
- 1492
 
3.7%
5 1492
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20145
 
11.8%
e 18659
 
10.9%
13568
 
7.9%
r 11081
 
6.5%
t 10949
 
6.4%
u 10667
 
6.2%
o 10385
 
6.1%
s 8274
 
4.8%
f 7992
 
4.7%
C 6389
 
3.7%
Other values (16) 53301
31.1%

expiration
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
1d
7091 
2h
5593 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters25368
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2h
2nd row1d
3rd row1d
4th row1d
5th row2h

Common Values

ValueCountFrequency (%)
1d 7091
55.9%
2h 5593
44.1%

Length

2022-12-02T23:40:09.093945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:09.179715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1d 7091
55.9%
2h 5593
44.1%

Most occurring characters

ValueCountFrequency (%)
1 7091
28.0%
d 7091
28.0%
2 5593
22.0%
h 5593
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
50.0%
Lowercase Letter 12684
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7091
55.9%
2 5593
44.1%
Lowercase Letter
ValueCountFrequency (%)
d 7091
55.9%
h 5593
44.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
50.0%
Latin 12684
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7091
55.9%
2 5593
44.1%
Latin
ValueCountFrequency (%)
d 7091
55.9%
h 5593
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7091
28.0%
d 7091
28.0%
2 5593
22.0%
h 5593
22.0%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
Female
6511 
Male
6173 

Length

Max length6
Median length6
Mean length5.0266477
Min length4

Characters and Unicode

Total characters63758
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 6511
51.3%
Male 6173
48.7%

Length

2022-12-02T23:40:09.262494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:09.358237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
female 6511
51.3%
male 6173
48.7%

Most occurring characters

ValueCountFrequency (%)
e 19195
30.1%
a 12684
19.9%
l 12684
19.9%
F 6511
 
10.2%
m 6511
 
10.2%
M 6173
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51074
80.1%
Uppercase Letter 12684
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19195
37.6%
a 12684
24.8%
l 12684
24.8%
m 6511
 
12.7%
Uppercase Letter
ValueCountFrequency (%)
F 6511
51.3%
M 6173
48.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 63758
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19195
30.1%
a 12684
19.9%
l 12684
19.9%
F 6511
 
10.2%
m 6511
 
10.2%
M 6173
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 19195
30.1%
a 12684
19.9%
l 12684
19.9%
F 6511
 
10.2%
m 6511
 
10.2%
M 6173
 
9.7%

age
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
26-30
2653 
21-25
2559 
31-35
2039 
36-40
1788 
<21
1319 
Other values (3)
2326 

Length

Max length5
Median length5
Mean length4.7057711
Min length3

Characters and Unicode

Total characters59688
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21-25
2nd row46-49
3rd row26-30
4th row21-25
5th row31-35

Common Values

ValueCountFrequency (%)
26-30 2653
20.9%
21-25 2559
20.2%
31-35 2039
16.1%
36-40 1788
14.1%
<21 1319
10.4%
41-45 1093
8.6%
46-49 686
 
5.4%
50+ 547
 
4.3%

Length

2022-12-02T23:40:09.440022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:09.553715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
26-30 2653
20.9%
21-25 2559
20.2%
31-35 2039
16.1%
36-40 1788
14.1%
21 1319
10.4%
41-45 1093
8.6%
46-49 686
 
5.4%
50 547
 
4.3%

Most occurring characters

ValueCountFrequency (%)
- 10818
18.1%
2 9090
15.2%
3 8519
14.3%
1 7010
11.7%
5 6238
10.5%
4 5346
9.0%
6 5127
8.6%
0 4988
8.4%
< 1319
 
2.2%
9 686
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47004
78.7%
Dash Punctuation 10818
 
18.1%
Math Symbol 1866
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9090
19.3%
3 8519
18.1%
1 7010
14.9%
5 6238
13.3%
4 5346
11.4%
6 5127
10.9%
0 4988
10.6%
9 686
 
1.5%
Math Symbol
ValueCountFrequency (%)
< 1319
70.7%
+ 547
29.3%
Dash Punctuation
ValueCountFrequency (%)
- 10818
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59688
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 10818
18.1%
2 9090
15.2%
3 8519
14.3%
1 7010
11.7%
5 6238
10.5%
4 5346
9.0%
6 5127
8.6%
0 4988
8.4%
< 1319
 
2.2%
9 686
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 10818
18.1%
2 9090
15.2%
3 8519
14.3%
1 7010
11.7%
5 6238
10.5%
4 5346
9.0%
6 5127
8.6%
0 4988
8.4%
< 1319
 
2.2%
9 686
 
1.1%

maritalStatus
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
Married partner
5100 
Single
4752 
Unmarried partner
2186 
Divorced
516 
Widowed
 
130

Length

Max length17
Median length15
Mean length11.606118
Min length6

Characters and Unicode

Total characters147212
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried partner
3rd rowSingle
4th rowUnmarried partner
5th rowSingle

Common Values

ValueCountFrequency (%)
Married partner 5100
40.2%
Single 4752
37.5%
Unmarried partner 2186
17.2%
Divorced 516
 
4.1%
Widowed 130
 
1.0%

Length

2022-12-02T23:40:09.670402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:09.772130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
partner 7286
36.5%
married 5100
25.5%
single 4752
23.8%
unmarried 2186
 
10.9%
divorced 516
 
2.6%
widowed 130
 
0.7%

Most occurring characters

ValueCountFrequency (%)
r 29660
20.1%
e 19970
13.6%
a 14572
9.9%
n 14224
9.7%
i 12684
8.6%
d 8062
 
5.5%
7286
 
4.9%
p 7286
 
4.9%
t 7286
 
4.9%
M 5100
 
3.5%
Other values (11) 21082
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127242
86.4%
Uppercase Letter 12684
 
8.6%
Space Separator 7286
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 29660
23.3%
e 19970
15.7%
a 14572
11.5%
n 14224
11.2%
i 12684
10.0%
d 8062
 
6.3%
p 7286
 
5.7%
t 7286
 
5.7%
l 4752
 
3.7%
g 4752
 
3.7%
Other values (5) 3994
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
M 5100
40.2%
S 4752
37.5%
U 2186
17.2%
D 516
 
4.1%
W 130
 
1.0%
Space Separator
ValueCountFrequency (%)
7286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 139926
95.1%
Common 7286
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 29660
21.2%
e 19970
14.3%
a 14572
10.4%
n 14224
10.2%
i 12684
9.1%
d 8062
 
5.8%
p 7286
 
5.2%
t 7286
 
5.2%
M 5100
 
3.6%
l 4752
 
3.4%
Other values (10) 16330
11.7%
Common
ValueCountFrequency (%)
7286
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 29660
20.1%
e 19970
13.6%
a 14572
9.9%
n 14224
9.7%
i 12684
8.6%
d 8062
 
5.5%
7286
 
4.9%
p 7286
 
4.9%
t 7286
 
4.9%
M 5100
 
3.5%
Other values (11) 21082
14.3%

has_children
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
7431 
1
5253 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 7431
58.6%
1 5253
41.4%

Length

2022-12-02T23:40:09.868840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:09.954644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7431
58.6%
1 5253
41.4%

Most occurring characters

ValueCountFrequency (%)
0 7431
58.6%
1 5253
41.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7431
58.6%
1 5253
41.4%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7431
58.6%
1 5253
41.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7431
58.6%
1 5253
41.4%

education
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
Some college - no degree
4351 
Bachelors degree
4335 
Graduate degree (Masters or Doctorate)
1852 
Associates degree
1153 
High School Graduate
905 

Length

Max length38
Median length24
Mean length22.332781
Min length16

Characters and Unicode

Total characters283269
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachelors degree
2nd rowGraduate degree (Masters or Doctorate)
3rd rowSome college - no degree
4th rowGraduate degree (Masters or Doctorate)
5th rowBachelors degree

Common Values

ValueCountFrequency (%)
Some college - no degree 4351
34.3%
Bachelors degree 4335
34.2%
Graduate degree (Masters or Doctorate) 1852
14.6%
Associates degree 1153
 
9.1%
High School Graduate 905
 
7.1%
Some High School 88
 
0.7%

Length

2022-12-02T23:40:10.031435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:10.134162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
degree 11691
26.0%
some 4439
 
9.9%
college 4351
 
9.7%
4351
 
9.7%
no 4351
 
9.7%
bachelors 4335
 
9.6%
graduate 2757
 
6.1%
masters 1852
 
4.1%
or 1852
 
4.1%
doctorate 1852
 
4.1%
Other values (3) 3139
 
7.0%

Most occurring characters

ValueCountFrequency (%)
e 60163
21.2%
32286
11.4%
o 26171
9.2%
r 24339
8.6%
g 17035
 
6.0%
a 14706
 
5.2%
d 14448
 
5.1%
l 14030
 
5.0%
c 12684
 
4.5%
s 11498
 
4.1%
Other values (16) 55909
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 224554
79.3%
Space Separator 32286
 
11.4%
Uppercase Letter 18374
 
6.5%
Dash Punctuation 4351
 
1.5%
Open Punctuation 1852
 
0.7%
Close Punctuation 1852
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 60163
26.8%
o 26171
11.7%
r 24339
10.8%
g 17035
 
7.6%
a 14706
 
6.5%
d 14448
 
6.4%
l 14030
 
6.2%
c 12684
 
5.6%
s 11498
 
5.1%
t 9466
 
4.2%
Other values (5) 20014
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
S 5432
29.6%
B 4335
23.6%
G 2757
15.0%
M 1852
 
10.1%
D 1852
 
10.1%
A 1153
 
6.3%
H 993
 
5.4%
Space Separator
ValueCountFrequency (%)
32286
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4351
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1852
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1852
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 242928
85.8%
Common 40341
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 60163
24.8%
o 26171
10.8%
r 24339
10.0%
g 17035
 
7.0%
a 14706
 
6.1%
d 14448
 
5.9%
l 14030
 
5.8%
c 12684
 
5.2%
s 11498
 
4.7%
t 9466
 
3.9%
Other values (12) 38388
15.8%
Common
ValueCountFrequency (%)
32286
80.0%
- 4351
 
10.8%
( 1852
 
4.6%
) 1852
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 60163
21.2%
32286
11.4%
o 26171
9.2%
r 24339
8.6%
g 17035
 
6.0%
a 14706
 
5.2%
d 14448
 
5.1%
l 14030
 
5.0%
c 12684
 
4.5%
s 11498
 
4.1%
Other values (16) 55909
19.7%

occupation
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
Unemployed
1870 
Student
1584 
Computer & Mathematical
1408 
Sales & Related
1093 
Education&Training&Library
943 
Other values (20)
5786 

Length

Max length41
Median length36
Mean length18.81859
Min length5

Characters and Unicode

Total characters238695
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStudent
2nd rowManagement
3rd rowSales & Related
4th rowEducation&Training&Library
5th rowProduction Occupations

Common Values

ValueCountFrequency (%)
Unemployed 1870
14.7%
Student 1584
12.5%
Computer & Mathematical 1408
11.1%
Sales & Related 1093
8.6%
Education&Training&Library 943
 
7.4%
Management 838
 
6.6%
Office & Administrative Support 639
 
5.0%
Arts Design Entertainment Sports & Media 629
 
5.0%
Business & Financial 544
 
4.3%
Retired 495
 
3.9%
Other values (15) 2641
20.8%

Length

2022-12-02T23:40:10.260826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6082
20.2%
unemployed 1870
 
6.2%
student 1584
 
5.3%
computer 1408
 
4.7%
mathematical 1408
 
4.7%
related 1391
 
4.6%
sales 1093
 
3.6%
education&training&library 943
 
3.1%
support 881
 
2.9%
management 838
 
2.8%
Other values (45) 12607
41.9%

Most occurring characters

ValueCountFrequency (%)
e 25178
 
10.5%
t 20552
 
8.6%
a 19380
 
8.1%
17421
 
7.3%
i 17417
 
7.3%
n 17336
 
7.3%
r 13026
 
5.5%
o 9537
 
4.0%
l 8583
 
3.6%
d 8047
 
3.4%
Other values (30) 82218
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 187397
78.5%
Uppercase Letter 25909
 
10.9%
Space Separator 17421
 
7.3%
Other Punctuation 7968
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 25178
13.4%
t 20552
11.0%
a 19380
10.3%
i 17417
9.3%
n 17336
9.3%
r 13026
 
7.0%
o 9537
 
5.1%
l 8583
 
4.6%
d 8047
 
4.3%
c 7604
 
4.1%
Other values (11) 40737
21.7%
Uppercase Letter
ValueCountFrequency (%)
S 5657
21.8%
M 3488
13.5%
C 2022
 
7.8%
R 2019
 
7.8%
E 1901
 
7.3%
U 1870
 
7.2%
A 1443
 
5.6%
T 1405
 
5.4%
L 1332
 
5.1%
P 1172
 
4.5%
Other values (7) 3600
13.9%
Space Separator
ValueCountFrequency (%)
17421
100.0%
Other Punctuation
ValueCountFrequency (%)
& 7968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 213306
89.4%
Common 25389
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25178
 
11.8%
t 20552
 
9.6%
a 19380
 
9.1%
i 17417
 
8.2%
n 17336
 
8.1%
r 13026
 
6.1%
o 9537
 
4.5%
l 8583
 
4.0%
d 8047
 
3.8%
c 7604
 
3.6%
Other values (28) 66646
31.2%
Common
ValueCountFrequency (%)
17421
68.6%
& 7968
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 25178
 
10.5%
t 20552
 
8.6%
a 19380
 
8.1%
17421
 
7.3%
i 17417
 
7.3%
n 17336
 
7.3%
r 13026
 
5.5%
o 9537
 
4.0%
l 8583
 
3.6%
d 8047
 
3.4%
Other values (30) 82218
34.4%

income
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
$25000 - $37499
2013 
$12500 - $24999
1831 
$37500 - $49999
1805 
$100000 or More
1736 
$50000 - $62499
1659 
Other values (4)
3640 

Length

Max length16
Median length15
Mean length15.082151
Min length15

Characters and Unicode

Total characters191302
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$12500 - $24999
2nd row$87500 - $99999
3rd row$37500 - $49999
4th row$37500 - $49999
5th row$37500 - $49999

Common Values

ValueCountFrequency (%)
$25000 - $37499 2013
15.9%
$12500 - $24999 1831
14.4%
$37500 - $49999 1805
14.2%
$100000 or More 1736
13.7%
$50000 - $62499 1659
13.1%
Less than $12500 1042
8.2%
$87500 - $99999 895
7.1%
$75000 - $87499 857
6.8%
$62500 - $74999 846
6.7%

Length

2022-12-02T23:40:10.357563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:10.721589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
9906
26.0%
12500 2873
 
7.6%
25000 2013
 
5.3%
37499 2013
 
5.3%
24999 1831
 
4.8%
37500 1805
 
4.7%
49999 1805
 
4.7%
100000 1736
 
4.6%
or 1736
 
4.6%
more 1736
 
4.6%
Other values (10) 10598
27.9%

Most occurring characters

ValueCountFrequency (%)
0 36764
19.2%
9 28784
15.0%
25368
13.3%
$ 22590
11.8%
5 10948
 
5.7%
- 9906
 
5.2%
2 9222
 
4.8%
4 9011
 
4.7%
7 7273
 
3.8%
1 4609
 
2.4%
Other values (13) 26827
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114686
60.0%
Space Separator 25368
 
13.3%
Currency Symbol 22590
 
11.8%
Lowercase Letter 15974
 
8.4%
Dash Punctuation 9906
 
5.2%
Uppercase Letter 2778
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36764
32.1%
9 28784
25.1%
5 10948
 
9.5%
2 9222
 
8.0%
4 9011
 
7.9%
7 7273
 
6.3%
1 4609
 
4.0%
3 3818
 
3.3%
6 2505
 
2.2%
8 1752
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
r 3472
21.7%
o 3472
21.7%
e 2778
17.4%
s 2084
13.0%
t 1042
 
6.5%
h 1042
 
6.5%
a 1042
 
6.5%
n 1042
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
M 1736
62.5%
L 1042
37.5%
Space Separator
ValueCountFrequency (%)
25368
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 22590
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 172550
90.2%
Latin 18752
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36764
21.3%
9 28784
16.7%
25368
14.7%
$ 22590
13.1%
5 10948
 
6.3%
- 9906
 
5.7%
2 9222
 
5.3%
4 9011
 
5.2%
7 7273
 
4.2%
1 4609
 
2.7%
Other values (3) 8075
 
4.7%
Latin
ValueCountFrequency (%)
r 3472
18.5%
o 3472
18.5%
e 2778
14.8%
s 2084
11.1%
M 1736
9.3%
L 1042
 
5.6%
t 1042
 
5.6%
h 1042
 
5.6%
a 1042
 
5.6%
n 1042
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36764
19.2%
9 28784
15.0%
25368
13.3%
$ 22590
11.8%
5 10948
 
5.7%
- 9906
 
5.2%
2 9222
 
4.8%
4 9011
 
4.7%
7 7273
 
3.8%
1 4609
 
2.4%
Other values (13) 26827
14.0%

car
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
no response
12576 
do not drive
 
22
Mazda5
 
22
Scooter and motorcycle
 
22
crossover
 
21

Length

Max length40
Median length11
Mean length11.056843
Min length6

Characters and Unicode

Total characters140245
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno response
2nd rowno response
3rd rowno response
4th rowno response
5th rowno response

Common Values

ValueCountFrequency (%)
no response 12576
99.1%
do not drive 22
 
0.2%
Mazda5 22
 
0.2%
Scooter and motorcycle 22
 
0.2%
crossover 21
 
0.2%
Car that is too old to install Onstar :D 21
 
0.2%

Length

2022-12-02T23:40:10.912077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:11.024779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no 12576
49.3%
response 12576
49.3%
do 22
 
0.1%
not 22
 
0.1%
drive 22
 
0.1%
mazda5 22
 
0.1%
scooter 22
 
0.1%
and 22
 
0.1%
motorcycle 22
 
0.1%
old 21
 
0.1%
Other values (9) 189
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 25410
18.1%
s 25257
18.0%
e 25239
18.0%
n 25238
18.0%
12832
9.1%
r 12726
9.1%
p 12576
9.0%
t 192
 
0.1%
a 150
 
0.1%
d 109
 
0.1%
Other values (15) 516
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127263
90.7%
Space Separator 12832
 
9.1%
Uppercase Letter 107
 
0.1%
Decimal Number 22
 
< 0.1%
Other Punctuation 21
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 25410
20.0%
s 25257
19.8%
e 25239
19.8%
n 25238
19.8%
r 12726
10.0%
p 12576
9.9%
t 192
 
0.2%
a 150
 
0.1%
d 109
 
0.1%
c 87
 
0.1%
Other values (7) 279
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
M 22
20.6%
S 22
20.6%
C 21
19.6%
O 21
19.6%
D 21
19.6%
Space Separator
ValueCountFrequency (%)
12832
100.0%
Decimal Number
ValueCountFrequency (%)
5 22
100.0%
Other Punctuation
ValueCountFrequency (%)
: 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 127370
90.8%
Common 12875
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 25410
19.9%
s 25257
19.8%
e 25239
19.8%
n 25238
19.8%
r 12726
10.0%
p 12576
9.9%
t 192
 
0.2%
a 150
 
0.1%
d 109
 
0.1%
c 87
 
0.1%
Other values (12) 386
 
0.3%
Common
ValueCountFrequency (%)
12832
99.7%
5 22
 
0.2%
: 21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 25410
18.1%
s 25257
18.0%
e 25239
18.0%
n 25238
18.0%
12832
9.1%
r 12726
9.1%
p 12576
9.0%
t 192
 
0.1%
a 150
 
0.1%
d 109
 
0.1%
Other values (15) 516
 
0.4%

Bar
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
1-3
5197 
never
3482 
<1
2473 
>8
1076 
no response
 
349

Length

Max length11
Median length5
Mean length3.4893567
Min length2

Characters and Unicode

Total characters44259
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownever
2nd rownever
3rd row<1
4th row1-3
5th rownever

Common Values

ValueCountFrequency (%)
1-3 5197
41.0%
never 3482
27.5%
<1 2473
19.5%
>8 1076
 
8.5%
no response 349
 
2.8%
4-8 107
 
0.8%

Length

2022-12-02T23:40:11.123514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:11.220257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1-3 5197
39.9%
never 3482
26.7%
1 2473
19.0%
8 1076
 
8.3%
no 349
 
2.7%
response 349
 
2.7%
4-8 107
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 7670
17.3%
e 7662
17.3%
- 5304
12.0%
3 5197
11.7%
n 4180
9.4%
r 3831
8.7%
v 3482
7.9%
< 2473
 
5.6%
8 1183
 
2.7%
> 1076
 
2.4%
Other values (5) 2201
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20900
47.2%
Decimal Number 14157
32.0%
Dash Punctuation 5304
 
12.0%
Math Symbol 3549
 
8.0%
Space Separator 349
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7662
36.7%
n 4180
20.0%
r 3831
18.3%
v 3482
16.7%
o 698
 
3.3%
s 698
 
3.3%
p 349
 
1.7%
Decimal Number
ValueCountFrequency (%)
1 7670
54.2%
3 5197
36.7%
8 1183
 
8.4%
4 107
 
0.8%
Math Symbol
ValueCountFrequency (%)
< 2473
69.7%
> 1076
30.3%
Dash Punctuation
ValueCountFrequency (%)
- 5304
100.0%
Space Separator
ValueCountFrequency (%)
349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23359
52.8%
Latin 20900
47.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7670
32.8%
- 5304
22.7%
3 5197
22.2%
< 2473
 
10.6%
8 1183
 
5.1%
> 1076
 
4.6%
349
 
1.5%
4 107
 
0.5%
Latin
ValueCountFrequency (%)
e 7662
36.7%
n 4180
20.0%
r 3831
18.3%
v 3482
16.7%
o 698
 
3.3%
s 698
 
3.3%
p 349
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7670
17.3%
e 7662
17.3%
- 5304
12.0%
3 5197
11.7%
n 4180
9.4%
r 3831
8.7%
v 3482
7.9%
< 2473
 
5.6%
8 1183
 
2.7%
> 1076
 
2.4%
Other values (5) 2201
 
5.0%

CoffeeHouse
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
never
3385 
<1
3225 
1-3
2962 
>8
1784 
no response
1111 

Length

Max length11
Median length5
Mean length3.8395617
Min length2

Characters and Unicode

Total characters48701
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno response
2nd row<1
3rd rowno response
4th row<1
5th rownever

Common Values

ValueCountFrequency (%)
never 3385
26.7%
<1 3225
25.4%
1-3 2962
23.4%
>8 1784
14.1%
no response 1111
 
8.8%
4-8 217
 
1.7%

Length

2022-12-02T23:40:11.322950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:11.424708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never 3385
24.5%
1 3225
23.4%
1-3 2962
21.5%
8 1784
12.9%
no 1111
 
8.1%
response 1111
 
8.1%
4-8 217
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 8992
18.5%
1 6187
12.7%
n 5607
11.5%
r 4496
9.2%
v 3385
 
7.0%
< 3225
 
6.6%
- 3179
 
6.5%
3 2962
 
6.1%
o 2222
 
4.6%
s 2222
 
4.6%
Other values (5) 6224
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28035
57.6%
Decimal Number 11367
23.3%
Math Symbol 5009
 
10.3%
Dash Punctuation 3179
 
6.5%
Space Separator 1111
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8992
32.1%
n 5607
20.0%
r 4496
16.0%
v 3385
 
12.1%
o 2222
 
7.9%
s 2222
 
7.9%
p 1111
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 6187
54.4%
3 2962
26.1%
8 2001
 
17.6%
4 217
 
1.9%
Math Symbol
ValueCountFrequency (%)
< 3225
64.4%
> 1784
35.6%
Dash Punctuation
ValueCountFrequency (%)
- 3179
100.0%
Space Separator
ValueCountFrequency (%)
1111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28035
57.6%
Common 20666
42.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6187
29.9%
< 3225
15.6%
- 3179
15.4%
3 2962
14.3%
8 2001
 
9.7%
> 1784
 
8.6%
1111
 
5.4%
4 217
 
1.1%
Latin
ValueCountFrequency (%)
e 8992
32.1%
n 5607
20.0%
r 4496
16.0%
v 3385
 
12.1%
o 2222
 
7.9%
s 2222
 
7.9%
p 1111
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8992
18.5%
1 6187
12.7%
n 5607
11.5%
r 4496
9.2%
v 3385
 
7.0%
< 3225
 
6.6%
- 3179
 
6.5%
3 2962
 
6.1%
o 2222
 
4.6%
s 2222
 
4.6%
Other values (5) 6224
12.8%

CarryAway
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
<1
4672 
>8
4258 
never
1856 
no response
1594 
1-3
 
153

Length

Max length11
Median length2
Mean length3.5939767
Min length2

Characters and Unicode

Total characters45586
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno response
2nd row<1
3rd row>8
4th row<1
5th row>8

Common Values

ValueCountFrequency (%)
<1 4672
36.8%
>8 4258
33.6%
never 1856
 
14.6%
no response 1594
 
12.6%
1-3 153
 
1.2%
4-8 151
 
1.2%

Length

2022-12-02T23:40:11.536381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:11.643094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4672
32.7%
8 4258
29.8%
never 1856
 
13.0%
no 1594
 
11.2%
response 1594
 
11.2%
1-3 153
 
1.1%
4-8 151
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 6900
15.1%
n 5044
11.1%
1 4825
10.6%
< 4672
10.2%
8 4409
9.7%
> 4258
9.3%
r 3450
7.6%
o 3188
7.0%
s 3188
7.0%
v 1856
 
4.1%
Other values (5) 3796
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25220
55.3%
Decimal Number 9538
 
20.9%
Math Symbol 8930
 
19.6%
Space Separator 1594
 
3.5%
Dash Punctuation 304
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6900
27.4%
n 5044
20.0%
r 3450
13.7%
o 3188
12.6%
s 3188
12.6%
v 1856
 
7.4%
p 1594
 
6.3%
Decimal Number
ValueCountFrequency (%)
1 4825
50.6%
8 4409
46.2%
3 153
 
1.6%
4 151
 
1.6%
Math Symbol
ValueCountFrequency (%)
< 4672
52.3%
> 4258
47.7%
Space Separator
ValueCountFrequency (%)
1594
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25220
55.3%
Common 20366
44.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4825
23.7%
< 4672
22.9%
8 4409
21.6%
> 4258
20.9%
1594
 
7.8%
- 304
 
1.5%
3 153
 
0.8%
4 151
 
0.7%
Latin
ValueCountFrequency (%)
e 6900
27.4%
n 5044
20.0%
r 3450
13.7%
o 3188
12.6%
s 3188
12.6%
v 1856
 
7.4%
p 1594
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6900
15.1%
n 5044
11.1%
1 4825
10.6%
< 4672
10.2%
8 4409
9.7%
> 4258
9.3%
r 3450
7.6%
o 3188
7.0%
s 3188
7.0%
v 1856
 
4.1%
Other values (5) 3796
8.3%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
<1
5376 
>8
3580 
never
2093 
no response
1285 
1-3
 
220

Length

Max length11
Median length2
Mean length3.4344056
Min length2

Characters and Unicode

Total characters43562
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno response
2nd rownever
3rd row<1
4th row<1
5th row4-8

Common Values

ValueCountFrequency (%)
<1 5376
42.4%
>8 3580
28.2%
never 2093
 
16.5%
no response 1285
 
10.1%
1-3 220
 
1.7%
4-8 130
 
1.0%

Length

2022-12-02T23:40:11.748842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:11.852533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5376
38.5%
8 3580
25.6%
never 2093
 
15.0%
no 1285
 
9.2%
response 1285
 
9.2%
1-3 220
 
1.6%
4-8 130
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 6756
15.5%
1 5596
12.8%
< 5376
12.3%
n 4663
10.7%
8 3710
8.5%
> 3580
8.2%
r 3378
7.8%
o 2570
 
5.9%
s 2570
 
5.9%
v 2093
 
4.8%
Other values (5) 3270
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23315
53.5%
Decimal Number 9656
22.2%
Math Symbol 8956
 
20.6%
Space Separator 1285
 
2.9%
Dash Punctuation 350
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6756
29.0%
n 4663
20.0%
r 3378
14.5%
o 2570
 
11.0%
s 2570
 
11.0%
v 2093
 
9.0%
p 1285
 
5.5%
Decimal Number
ValueCountFrequency (%)
1 5596
58.0%
8 3710
38.4%
3 220
 
2.3%
4 130
 
1.3%
Math Symbol
ValueCountFrequency (%)
< 5376
60.0%
> 3580
40.0%
Space Separator
ValueCountFrequency (%)
1285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23315
53.5%
Common 20247
46.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5596
27.6%
< 5376
26.6%
8 3710
18.3%
> 3580
17.7%
1285
 
6.3%
- 350
 
1.7%
3 220
 
1.1%
4 130
 
0.6%
Latin
ValueCountFrequency (%)
e 6756
29.0%
n 4663
20.0%
r 3378
14.5%
o 2570
 
11.0%
s 2570
 
11.0%
v 2093
 
9.0%
p 1285
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6756
15.5%
1 5596
12.8%
< 5376
12.3%
n 4663
10.7%
8 3710
8.5%
> 3580
8.2%
r 3378
7.8%
o 2570
 
5.9%
s 2570
 
5.9%
v 2093
 
4.8%
Other values (5) 3270
7.5%

Restaurant20To50
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
never
6077 
<1
3290 
1-3
2136 
>8
728 
no response
 
264

Length

Max length11
Median length5
Mean length3.807947
Min length2

Characters and Unicode

Total characters48300
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownever
2nd row<1
3rd rownever
4th rownever
5th rownever

Common Values

ValueCountFrequency (%)
never 6077
47.9%
<1 3290
25.9%
1-3 2136
 
16.8%
>8 728
 
5.7%
no response 264
 
2.1%
4-8 189
 
1.5%

Length

2022-12-02T23:40:11.966255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:12.067988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never 6077
46.9%
1 3290
25.4%
1-3 2136
 
16.5%
8 728
 
5.6%
no 264
 
2.0%
response 264
 
2.0%
4-8 189
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 12682
26.3%
n 6605
13.7%
r 6341
13.1%
v 6077
12.6%
1 5426
11.2%
< 3290
 
6.8%
- 2325
 
4.8%
3 2136
 
4.4%
8 917
 
1.9%
> 728
 
1.5%
Other values (5) 1773
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33025
68.4%
Decimal Number 8668
 
17.9%
Math Symbol 4018
 
8.3%
Dash Punctuation 2325
 
4.8%
Space Separator 264
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12682
38.4%
n 6605
20.0%
r 6341
19.2%
v 6077
18.4%
o 528
 
1.6%
s 528
 
1.6%
p 264
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 5426
62.6%
3 2136
 
24.6%
8 917
 
10.6%
4 189
 
2.2%
Math Symbol
ValueCountFrequency (%)
< 3290
81.9%
> 728
 
18.1%
Dash Punctuation
ValueCountFrequency (%)
- 2325
100.0%
Space Separator
ValueCountFrequency (%)
264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33025
68.4%
Common 15275
31.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5426
35.5%
< 3290
21.5%
- 2325
15.2%
3 2136
 
14.0%
8 917
 
6.0%
> 728
 
4.8%
264
 
1.7%
4 189
 
1.2%
Latin
ValueCountFrequency (%)
e 12682
38.4%
n 6605
20.0%
r 6341
19.2%
v 6077
18.4%
o 528
 
1.6%
s 528
 
1.6%
p 264
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12682
26.3%
n 6605
13.7%
r 6341
13.1%
v 6077
12.6%
1 5426
11.2%
< 3290
 
6.8%
- 2325
 
4.8%
3 2136
 
4.4%
8 917
 
1.9%
> 728
 
1.5%
Other values (5) 1773
 
3.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
1
7122 
0
5562 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7122
56.1%
0 5562
43.9%

Length

2022-12-02T23:40:12.369180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:12.458939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7122
56.1%
0 5562
43.9%

Most occurring characters

ValueCountFrequency (%)
1 7122
56.1%
0 5562
43.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7122
56.1%
0 5562
43.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7122
56.1%
0 5562
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7122
56.1%
0 5562
43.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
11173 
1
1511 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11173
88.1%
1 1511
 
11.9%

Length

2022-12-02T23:40:12.537730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:12.624497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11173
88.1%
1 1511
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 11173
88.1%
1 1511
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11173
88.1%
1 1511
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11173
88.1%
1 1511
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11173
88.1%
1 1511
 
11.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
1
9960 
0
2724 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9960
78.5%
0 2724
 
21.5%

Length

2022-12-02T23:40:12.700296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:12.784074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9960
78.5%
0 2724
 
21.5%

Most occurring characters

ValueCountFrequency (%)
1 9960
78.5%
0 2724
 
21.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9960
78.5%
0 2724
 
21.5%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9960
78.5%
0 2724
 
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9960
78.5%
0 2724
 
21.5%

Y
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
1
7210 
0
5474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7210
56.8%
0 5474
43.2%

Length

2022-12-02T23:40:12.858875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:12.943647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7210
56.8%
0 5474
43.2%

Most occurring characters

ValueCountFrequency (%)
1 7210
56.8%
0 5474
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7210
56.8%
0 5474
43.2%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7210
56.8%
0 5474
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7210
56.8%
0 5474
43.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
24
7091 
2
5593 

Length

Max length2
Median length2
Mean length1.5590508
Min length1

Characters and Unicode

Total characters19775
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row24
3rd row24
4th row24
5th row2

Common Values

ValueCountFrequency (%)
24 7091
55.9%
2 5593
44.1%

Length

2022-12-02T23:40:13.027390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:13.123134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
24 7091
55.9%
2 5593
44.1%

Most occurring characters

ValueCountFrequency (%)
2 12684
64.1%
4 7091
35.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19775
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12684
64.1%
4 7091
35.9%

Most occurring scripts

ValueCountFrequency (%)
Common 19775
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 12684
64.1%
4 7091
35.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 12684
64.1%
4 7091
35.9%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
18
3230 
7
3164 
10
2275 
14
2009 
22
2006 

Length

Max length2
Median length2
Mean length1.7505519
Min length1

Characters and Unicode

Total characters22204
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18
2nd row7
3rd row18
4th row10
5th row14

Common Values

ValueCountFrequency (%)
18 3230
25.5%
7 3164
24.9%
10 2275
17.9%
14 2009
15.8%
22 2006
15.8%

Length

2022-12-02T23:40:13.203918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:13.307640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
18 3230
25.5%
7 3164
24.9%
10 2275
17.9%
14 2009
15.8%
22 2006
15.8%

Most occurring characters

ValueCountFrequency (%)
1 7514
33.8%
2 4012
18.1%
8 3230
14.5%
7 3164
14.2%
0 2275
 
10.2%
4 2009
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22204
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7514
33.8%
2 4012
18.1%
8 3230
14.5%
7 3164
14.2%
0 2275
 
10.2%
4 2009
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22204
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7514
33.8%
2 4012
18.1%
8 3230
14.5%
7 3164
14.2%
0 2275
 
10.2%
4 2009
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7514
33.8%
2 4012
18.1%
8 3230
14.5%
7 3164
14.2%
0 2275
 
10.2%
4 2009
 
9.0%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.746531
Minimum18
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:13.404411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q123
median28
Q338
95-th percentile48
Maximum56
Range38
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.7139734
Coefficient of variation (CV)0.30598535
Kurtosis-0.16564168
Mean31.746531
Median Absolute Deviation (MAD)5
Skewness0.64267205
Sum402673
Variance94.361279
MonotonicityNot monotonic
2022-12-02T23:40:13.477212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
28 2653
20.9%
23 2559
20.2%
33 2039
16.1%
38 1788
14.1%
18 1319
10.4%
43 1093
8.6%
48 686
 
5.4%
56 547
 
4.3%
ValueCountFrequency (%)
18 1319
10.4%
23 2559
20.2%
28 2653
20.9%
33 2039
16.1%
38 1788
14.1%
43 1093
8.6%
48 686
 
5.4%
56 547
 
4.3%
ValueCountFrequency (%)
56 547
 
4.3%
48 686
 
5.4%
43 1093
8.6%
38 1788
14.1%
33 2039
16.1%
28 2653
20.9%
23 2559
20.2%
18 1319
10.4%
Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58982.383
Minimum6250
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:13.557004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6250
5-th percentile6250
Q131249.5
median43749.5
Q381249.5
95-th percentile150000
Maximum150000
Range143750
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation43327.639
Coefficient of variation (CV)0.73458611
Kurtosis0.01671023
Mean58982.383
Median Absolute Deviation (MAD)25000
Skewness1.0038639
Sum7.4813255 × 108
Variance1.8772843 × 109
MonotonicityNot monotonic
2022-12-02T23:40:13.631803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
31249.5 2013
15.9%
18749.5 1831
14.4%
43749.5 1805
14.2%
150000 1736
13.7%
56249.5 1659
13.1%
6250 1042
8.2%
93749.5 895
7.1%
81249.5 857
6.8%
68749.5 846
6.7%
ValueCountFrequency (%)
6250 1042
8.2%
18749.5 1831
14.4%
31249.5 2013
15.9%
43749.5 1805
14.2%
56249.5 1659
13.1%
68749.5 846
6.7%
81249.5 857
6.8%
93749.5 895
7.1%
150000 1736
13.7%
ValueCountFrequency (%)
150000 1736
13.7%
93749.5 895
7.1%
81249.5 857
6.8%
68749.5 846
6.7%
56249.5 1659
13.1%
43749.5 1805
14.2%
31249.5 2013
15.9%
18749.5 1831
14.4%
6250 1042
8.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
6511 
1
6173 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6511
51.3%
1 6173
48.7%

Length

2022-12-02T23:40:13.721563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:13.805341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6511
51.3%
1 6173
48.7%

Most occurring characters

ValueCountFrequency (%)
0 6511
51.3%
1 6173
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6511
51.3%
1 6173
48.7%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6511
51.3%
1 6173
48.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6511
51.3%
1 6173
48.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
1
7091 
0
5593 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 7091
55.9%
0 5593
44.1%

Length

2022-12-02T23:40:13.879141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:13.963915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7091
55.9%
0 5593
44.1%

Most occurring characters

ValueCountFrequency (%)
1 7091
55.9%
0 5593
44.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7091
55.9%
0 5593
44.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7091
55.9%
0 5593
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7091
55.9%
0 5593
44.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
1
3996 
4
2786 
3
2393 
2
2017 
5
1492 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row4

Common Values

ValueCountFrequency (%)
1 3996
31.5%
4 2786
22.0%
3 2393
18.9%
2 2017
15.9%
5 1492
 
11.8%

Length

2022-12-02T23:40:14.038714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:14.132432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3996
31.5%
4 2786
22.0%
3 2393
18.9%
2 2017
15.9%
5 1492
 
11.8%

Most occurring characters

ValueCountFrequency (%)
1 3996
31.5%
4 2786
22.0%
3 2393
18.9%
2 2017
15.9%
5 1492
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3996
31.5%
4 2786
22.0%
3 2393
18.9%
2 2017
15.9%
5 1492
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3996
31.5%
4 2786
22.0%
3 2393
18.9%
2 2017
15.9%
5 1492
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3996
31.5%
4 2786
22.0%
3 2393
18.9%
2 2017
15.9%
5 1492
 
11.8%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1272469
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:14.215211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2651549
Coefficient of variation (CV)0.30653724
Kurtosis-1.1758028
Mean4.1272469
Median Absolute Deviation (MAD)1
Skewness-0.10092568
Sum52350
Variance1.6006168
MonotonicityNot monotonic
2022-12-02T23:40:14.287047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 4351
34.3%
5 4335
34.2%
6 1852
14.6%
4 1153
 
9.1%
2 905
 
7.1%
1 88
 
0.7%
ValueCountFrequency (%)
1 88
 
0.7%
2 905
 
7.1%
3 4351
34.3%
4 1153
 
9.1%
5 4335
34.2%
6 1852
14.6%
ValueCountFrequency (%)
6 1852
14.6%
5 4335
34.2%
4 1153
 
9.1%
3 4351
34.3%
2 905
 
7.1%
1 88
 
0.7%
Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7395932
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:14.359859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5466224
Coefficient of variation (CV)0.53730822
Kurtosis-1.0811082
Mean4.7395932
Median Absolute Deviation (MAD)2
Skewness0.35055729
Sum60117
Variance6.4852855
MonotonicityNot monotonic
2022-12-02T23:40:14.436646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 2013
15.9%
2 1831
14.4%
4 1805
14.2%
9 1736
13.7%
5 1659
13.1%
1 1042
8.2%
8 895
7.1%
7 857
6.8%
6 846
6.7%
ValueCountFrequency (%)
1 1042
8.2%
2 1831
14.4%
3 2013
15.9%
4 1805
14.2%
5 1659
13.1%
6 846
6.7%
7 857
6.8%
8 895
7.1%
9 1736
13.7%
ValueCountFrequency (%)
9 1736
13.7%
8 895
7.1%
7 857
6.8%
6 846
6.7%
5 1659
13.1%
4 1805
14.2%
3 2013
15.9%
2 1831
14.4%
1 1042
8.2%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7234311
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:14.520394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8810273
Coefficient of variation (CV)0.50518653
Kurtosis-0.53064156
Mean3.7234311
Median Absolute Deviation (MAD)1
Skewness0.50742228
Sum47228
Variance3.5382635
MonotonicityNot monotonic
2022-12-02T23:40:14.589211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 2653
20.9%
2 2559
20.2%
4 2039
16.1%
5 1788
14.1%
1 1319
10.4%
6 1093
8.6%
7 686
 
5.4%
8 547
 
4.3%
ValueCountFrequency (%)
1 1319
10.4%
2 2559
20.2%
3 2653
20.9%
4 2039
16.1%
5 1788
14.1%
6 1093
8.6%
7 686
 
5.4%
8 547
 
4.3%
ValueCountFrequency (%)
8 547
 
4.3%
7 686
 
5.4%
6 1093
8.6%
5 1788
14.1%
4 2039
16.1%
3 2653
20.9%
2 2559
20.2%
1 1319
10.4%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
4
3230 
1
3164 
2
2275 
3
2009 
5
2006 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row2
5th row3

Common Values

ValueCountFrequency (%)
4 3230
25.5%
1 3164
24.9%
2 2275
17.9%
3 2009
15.8%
5 2006
15.8%

Length

2022-12-02T23:40:14.672986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:14.767764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 3230
25.5%
1 3164
24.9%
2 2275
17.9%
3 2009
15.8%
5 2006
15.8%

Most occurring characters

ValueCountFrequency (%)
4 3230
25.5%
1 3164
24.9%
2 2275
17.9%
3 2009
15.8%
5 2006
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 3230
25.5%
1 3164
24.9%
2 2275
17.9%
3 2009
15.8%
5 2006
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 3230
25.5%
1 3164
24.9%
2 2275
17.9%
3 2009
15.8%
5 2006
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3230
25.5%
1 3164
24.9%
2 2275
17.9%
3 2009
15.8%
5 2006
15.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
3
6528 
2
3840 
1
2316 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 6528
51.5%
2 3840
30.3%
1 2316
 
18.3%

Length

2022-12-02T23:40:14.861513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:14.950276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 6528
51.5%
2 3840
30.3%
1 2316
 
18.3%

Most occurring characters

ValueCountFrequency (%)
3 6528
51.5%
2 3840
30.3%
1 2316
 
18.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 6528
51.5%
2 3840
30.3%
1 2316
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 6528
51.5%
2 3840
30.3%
1 2316
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 6528
51.5%
2 3840
30.3%
1 2316
 
18.3%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3515453
Minimum0
Maximum5
Zeros349
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:15.022083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.211637
Coefficient of variation (CV)0.51525142
Kurtosis-0.18327323
Mean2.3515453
Median Absolute Deviation (MAD)1
Skewness0.38318011
Sum29827
Variance1.4680643
MonotonicityNot monotonic
2022-12-02T23:40:15.093894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 5197
41.0%
1 3482
27.5%
2 2473
19.5%
5 1076
 
8.5%
0 349
 
2.8%
4 107
 
0.8%
ValueCountFrequency (%)
0 349
 
2.8%
1 3482
27.5%
2 2473
19.5%
3 5197
41.0%
4 107
 
0.8%
5 1076
 
8.5%
ValueCountFrequency (%)
5 1076
 
8.5%
4 107
 
0.8%
3 5197
41.0%
2 2473
19.5%
1 3482
27.5%
0 349
 
2.8%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2476348
Minimum0
Maximum5
Zeros1111
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:15.164711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.457354
Coefficient of variation (CV)0.64839448
Kurtosis-0.51848374
Mean2.2476348
Median Absolute Deviation (MAD)1
Skewness0.51944485
Sum28509
Variance2.1238807
MonotonicityNot monotonic
2022-12-02T23:40:15.239505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3385
26.7%
2 3225
25.4%
3 2962
23.4%
5 1784
14.1%
0 1111
 
8.8%
4 217
 
1.7%
ValueCountFrequency (%)
0 1111
 
8.8%
1 3385
26.7%
2 3225
25.4%
3 2962
23.4%
4 217
 
1.7%
5 1784
14.1%
ValueCountFrequency (%)
5 1784
14.1%
4 217
 
1.7%
3 2962
23.4%
2 3225
25.4%
1 3385
26.7%
0 1111
 
8.8%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6453012
Minimum0
Maximum5
Zeros1594
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:15.313305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.8203907
Coefficient of variation (CV)0.68816011
Kurtosis-1.3925313
Mean2.6453012
Median Absolute Deviation (MAD)1
Skewness0.22152561
Sum33553
Variance3.3138224
MonotonicityNot monotonic
2022-12-02T23:40:15.385112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 4672
36.8%
5 4258
33.6%
1 1856
 
14.6%
0 1594
 
12.6%
3 153
 
1.2%
4 151
 
1.2%
ValueCountFrequency (%)
0 1594
 
12.6%
1 1856
 
14.6%
2 4672
36.8%
3 153
 
1.2%
4 151
 
1.2%
5 4258
33.6%
ValueCountFrequency (%)
5 4258
33.6%
4 151
 
1.2%
3 153
 
1.2%
2 4672
36.8%
1 1856
 
14.6%
0 1594
 
12.6%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5169505
Minimum0
Maximum5
Zeros1285
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:15.455891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.703463
Coefficient of variation (CV)0.6767964
Kurtosis-1.1292607
Mean2.5169505
Median Absolute Deviation (MAD)1
Skewness0.42624106
Sum31925
Variance2.9017863
MonotonicityNot monotonic
2022-12-02T23:40:15.527730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 5376
42.4%
5 3580
28.2%
1 2093
 
16.5%
0 1285
 
10.1%
3 220
 
1.7%
4 130
 
1.0%
ValueCountFrequency (%)
0 1285
 
10.1%
1 2093
 
16.5%
2 5376
42.4%
3 220
 
1.7%
4 130
 
1.0%
5 3580
28.2%
ValueCountFrequency (%)
5 3580
28.2%
4 130
 
1.0%
3 220
 
1.7%
2 5376
42.4%
1 2093
 
16.5%
0 1285
 
10.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8496531
Minimum0
Maximum5
Zeros264
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-12-02T23:40:15.598544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1333222
Coefficient of variation (CV)0.61272148
Kurtosis1.0993749
Mean1.8496531
Median Absolute Deviation (MAD)1
Skewness1.2192103
Sum23461
Variance1.2844192
MonotonicityNot monotonic
2022-12-02T23:40:15.672345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 6077
47.9%
2 3290
25.9%
3 2136
 
16.8%
5 728
 
5.7%
0 264
 
2.1%
4 189
 
1.5%
ValueCountFrequency (%)
0 264
 
2.1%
1 6077
47.9%
2 3290
25.9%
3 2136
 
16.8%
4 189
 
1.5%
5 728
 
5.7%
ValueCountFrequency (%)
5 728
 
5.7%
4 189
 
1.5%
3 2136
 
16.8%
2 3290
25.9%
1 6077
47.9%
0 264
 
2.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
12335 
1
 
349

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12335
97.2%
1 349
 
2.8%

Length

2022-12-02T23:40:15.753221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:15.834005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12335
97.2%
1 349
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 12335
97.2%
1 349
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12335
97.2%
1 349
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12335
97.2%
1 349
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12335
97.2%
1 349
 
2.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
11573 
1
 
1111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11573
91.2%
1 1111
 
8.8%

Length

2022-12-02T23:40:15.903818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:15.986597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11573
91.2%
1 1111
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 11573
91.2%
1 1111
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11573
91.2%
1 1111
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11573
91.2%
1 1111
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11573
91.2%
1 1111
 
8.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
11090 
1
1594 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11090
87.4%
1 1594
 
12.6%

Length

2022-12-02T23:40:16.060399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:16.143178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11090
87.4%
1 1594
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 11090
87.4%
1 1594
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11090
87.4%
1 1594
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11090
87.4%
1 1594
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11090
87.4%
1 1594
 
12.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
11399 
1
1285 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11399
89.9%
1 1285
 
10.1%

Length

2022-12-02T23:40:16.216980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:16.298761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11399
89.9%
1 1285
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 11399
89.9%
1 1285
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11399
89.9%
1 1285
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11399
89.9%
1 1285
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11399
89.9%
1 1285
 
10.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
12420 
1
 
264

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12420
97.9%
1 264
 
2.1%

Length

2022-12-02T23:40:16.373562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-02T23:40:16.455343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12420
97.9%
1 264
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 12420
97.9%
1 264
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12684
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12420
97.9%
1 264
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12420
97.9%
1 264
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12420
97.9%
1 264
 
2.1%

Interactions

2022-12-02T23:40:04.608309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:54.303527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.463455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.628477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.798672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.920339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.010577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.238326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.352311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.463339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.720008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:54.431217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.583104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.736546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.911399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.031042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.125300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.352985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.466034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.575068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.840684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:54.550897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.703813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.846222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.028085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.146919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.239999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.466712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.582726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.692756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.948397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:54.658609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.815511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.047682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.132780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.250641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.344717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.572429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.688443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.801461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:05.062061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:54.775296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.931343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.157419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.251462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.361347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.457410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.685131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.800146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.916127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:05.170770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:54.885997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.046067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.262142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.368150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.469058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.566090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.795832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.910848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.024868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:05.277484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:54.995707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.161758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.368838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.482842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.575773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.672805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.907529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.020555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.136569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:05.391181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.107408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.277448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.483546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.591187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.682486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:00.906210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.015246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.130261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.248238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:05.515875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.233069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.394137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.588267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.698931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.792192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.015918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.126943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.240968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.361936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:05.628586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:55.350758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:56.512820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:57.692983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:58.811598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:39:59.901903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:01.127616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:02.239644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:03.353663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-02T23:40:04.489592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-02T23:40:16.571033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-02T23:40:17.201378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-02T23:40:17.732956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-02T23:40:18.260540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-02T23:40:19.060373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-02T23:40:19.521174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-02T23:40:06.012558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-02T23:40:07.456694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

destinationpassengerweathertemperaturetimecoupon_venue_typeexpirationgenderagemaritalStatushas_childreneducationoccupationincomecarBarCoffeeHouseCarryAwayRestaurantLessThan20Restaurant20To50toCoupon_GEQ15mintoCoupon_GEQ25mindirection_same_or_oppositeYexpiration_category_representative_numeric_encodingtime_category_representative_numeric_encodingage_category_representative_numeric_encodingincome_category_representative_numeric_encodinggender_binary_encodingexpiration_binary_encodingcoupon_venue_type_ordinal_integer_encodingeducation_ordinal_integer_encodingincome_ordinal_integer_encodingage_ordinal_integer_encodingtime_ordinal_integer_encodingtemperature_ordinal_integer_encodingBar_venue_visit_frequency_yes_response_ordinal_integer_encodingCoffeeHouse_venue_visit_frequency_yes_response_ordinal_integer_encodingCarryAway_venue_visit_frequency_yes_response_ordinal_integer_encodingRestaurantLessThan20_venue_visit_frequency_yes_response_ordinal_integer_encodingRestaurant20To50_venue_visit_frequency_yes_response_ordinal_integer_encodingBar_venue_visit_frequency_no_response_indicatorCoffeeHouse_venue_visit_frequency_no_response_indicatorCarryAway_venue_visit_frequency_no_response_indicatorRestaurantLessThan20_venue_visit_frequency_no_response_indicatorRestaurant20To50_venue_visit_frequency_no_response_indicator
0HomeAloneSunny806PMCarry out & Take away2hMale21-25Single0Bachelors degreeStudent$12500 - $24999no responseneverno responseno responseno responsenever10012182318749.5103522431000101110
1WorkAloneSunny557AMBar1dMale46-49Married partner1Graduate degree (Masters or Doctorate)Management$87500 - $99999no responsenever<1<1never<111102474893749.5112687121221200000
2HomeAloneSunny306PMCarry out & Take away1dMale26-30Single0Some college - no degreeSales & Related$37500 - $49999no response<1no response>8<1never001124182843749.5113343412052101000
3No Urgent PlaceAloneSunny8010AMBar1dFemale21-25Unmarried partner0Graduate degree (Masters or Doctorate)Education&Training&Library$37500 - $49999no response1-3<1<1<1never001024102343749.5012642233222100000
4No Urgent PlaceAloneSunny802PMRestaurant(<20)2hFemale31-35Single1Bachelors degreeProduction Occupations$37500 - $49999no responsenevernever>84-8never10112143343749.5004544331154100000
5No Urgent PlaceFriend(s)Sunny8010AMCoffee House2hMale21-25Single0Bachelors degreeComputer & Mathematical$50000 - $62499no response1-3<1<1nevernever00112102356249.5101552233221100000
6No Urgent PlacePartnerRainy556PMBar2hFemale41-45Married partner1Graduate degree (Masters or Doctorate)Management$75000 - $87499no responsenever<1>8<11-310112184381249.5002676421252300000
7No Urgent PlaceAloneSunny552PMRestaurant(<20)1dMale21-25Single0Some college - no degreeUnemployed$87500 - $99999no response1-3<1>8never1-3001124142393749.5114382323251300000
8HomeAloneSunny8010PMRestaurant(<20)2hMale50+Single0Some college - no degreeStudent$87500 - $99999no response1-3>84-8>8never00002225693749.5104388533545100000
9No Urgent PlaceFriend(s)Sunny806PMRestaurant(<20)2hFemale31-35Single0Bachelors degreeSales & Related$25000 - $37499no response>8<1<1>8<110112183331249.5004534435225200000
destinationpassengerweathertemperaturetimecoupon_venue_typeexpirationgenderagemaritalStatushas_childreneducationoccupationincomecarBarCoffeeHouseCarryAwayRestaurantLessThan20Restaurant20To50toCoupon_GEQ15mintoCoupon_GEQ25mindirection_same_or_oppositeYexpiration_category_representative_numeric_encodingtime_category_representative_numeric_encodingage_category_representative_numeric_encodingincome_category_representative_numeric_encodinggender_binary_encodingexpiration_binary_encodingcoupon_venue_type_ordinal_integer_encodingeducation_ordinal_integer_encodingincome_ordinal_integer_encodingage_ordinal_integer_encodingtime_ordinal_integer_encodingtemperature_ordinal_integer_encodingBar_venue_visit_frequency_yes_response_ordinal_integer_encodingCoffeeHouse_venue_visit_frequency_yes_response_ordinal_integer_encodingCarryAway_venue_visit_frequency_yes_response_ordinal_integer_encodingRestaurantLessThan20_venue_visit_frequency_yes_response_ordinal_integer_encodingRestaurant20To50_venue_visit_frequency_yes_response_ordinal_integer_encodingBar_venue_visit_frequency_no_response_indicatorCoffeeHouse_venue_visit_frequency_no_response_indicatorCarryAway_venue_visit_frequency_no_response_indicatorRestaurantLessThan20_venue_visit_frequency_no_response_indicatorRestaurant20To50_venue_visit_frequency_no_response_indicator
12674HomeAloneSnowy3010PMRestaurant(20-50)2hFemale36-40Married partner1Some college - no degreeArts Design Entertainment Sports & Media$37500 - $49999no response1-3<11-3<1never00102223843749.5005345513232100000
12675No Urgent PlaceAloneSunny552PMRestaurant(<20)1dFemale31-35Unmarried partner1Associates degreeEducation&Training&Library$50000 - $62499no response1-31-3>84-81-3001124143356249.5014454323354300000
12676WorkAloneSunny807AMCoffee House1dFemale21-25Unmarried partner0Graduate degree (Masters or Doctorate)Computer & Mathematical$62500 - $74999no response<1never<1>8<100002472368749.5011662132125200000
12677WorkAloneRainy557AMRestaurant(<20)2hMale26-30Unmarried partner0Bachelors degreeBusiness & Financial$62500 - $74999no response>8<1>8<1never1110272868749.5104563125252100000
12678HomeAloneSunny306PMCarry out & Take away2hFemale36-40Married partner1Bachelors degreeEducation&Training&Library$12500 - $24999no responsenever>8>8no responsenever10012183818749.5003525411550100010
12679WorkAloneSunny807AMRestaurant(20-50)2hFemale36-40Single1Bachelors degreeFood Preparation & Serving Related$12500 - $24999no response1-3<1>8nevernever0000273818749.5005525133251100000
12680HomeAloneSnowy3010PMRestaurant(<20)2hMale26-30Single0Some college - no degreeStudent$12500 - $24999no response<1never>8never1-311102222818749.5104323512151300000
12681HomeAloneSunny806PMRestaurant(20-50)1dMale46-49Single0Some college - no degreeSales & RelatedLess than $12500no response<1<1>8>8>800002418486250.0115317432255500000
12682WorkAloneSunny807AMCarry out & Take away2hFemale21-25Single0Graduate degree (Masters or Doctorate)Legal$25000 - $37499no response<1<1<1no response<10001272331249.5003632132220200010
12683No Urgent PlaceKid(s)Sunny8010AMBar1dFemale36-40Married partner1Bachelors degreeRetired$50000 - $62499no response1-3never>8<1never101024103856249.5012555233152100000

Duplicate rows

Most frequently occurring

destinationpassengerweathertemperaturetimecoupon_venue_typeexpirationgenderagemaritalStatushas_childreneducationoccupationincomecarBarCoffeeHouseCarryAwayRestaurantLessThan20Restaurant20To50toCoupon_GEQ15mintoCoupon_GEQ25mindirection_same_or_oppositeYexpiration_category_representative_numeric_encodingtime_category_representative_numeric_encodingage_category_representative_numeric_encodingincome_category_representative_numeric_encodinggender_binary_encodingexpiration_binary_encodingcoupon_venue_type_ordinal_integer_encodingeducation_ordinal_integer_encodingincome_ordinal_integer_encodingage_ordinal_integer_encodingtime_ordinal_integer_encodingtemperature_ordinal_integer_encodingBar_venue_visit_frequency_yes_response_ordinal_integer_encodingCoffeeHouse_venue_visit_frequency_yes_response_ordinal_integer_encodingCarryAway_venue_visit_frequency_yes_response_ordinal_integer_encodingRestaurantLessThan20_venue_visit_frequency_yes_response_ordinal_integer_encodingRestaurant20To50_venue_visit_frequency_yes_response_ordinal_integer_encodingBar_venue_visit_frequency_no_response_indicatorCoffeeHouse_venue_visit_frequency_no_response_indicatorCarryAway_venue_visit_frequency_no_response_indicatorRestaurantLessThan20_venue_visit_frequency_no_response_indicatorRestaurant20To50_venue_visit_frequency_no_response_indicator# duplicates
0HomeAloneSnowy3010PMRestaurant(20-50)2hFemale31-35Married partner0Some college - no degreeComputer & Mathematical$100000 or Moreno responseneverneverno response>8never001022233150000.00053945111051001002
1HomeAloneSnowy306PMCoffee House1dMale46-49Married partner1Graduate degree (Masters or Doctorate)Management$87500 - $99999no responsenever<1<1never<1101024184893749.51116874112212000002
2HomeAloneSunny306PMCarry out & Take away2hMale46-49Married partner1Graduate degree (Masters or Doctorate)Management$87500 - $99999no responsenever<1<1never<110012184893749.51036874112212000002
3HomeAloneSunny806PMBar2hFemale31-35Married partner0Some college - no degreeComputer & Mathematical$100000 or Moreno responseneverneverno response>8never000021833150000.00023944311051001002
4HomeAloneSunny806PMBar2hMale46-49Married partner1Graduate degree (Masters or Doctorate)Management$87500 - $99999no responsenever<1<1never<100012184893749.51026874312212000002
5HomePartnerSunny3010PMCarry out & Take away2hMale46-49Married partner1Graduate degree (Masters or Doctorate)Management$87500 - $99999no responsenever<1<1never<110112224893749.51036875112212000002
6HomePartnerSunny806PMCoffee House1dFemale31-35Married partner0Some college - no degreeComputer & Mathematical$100000 or Moreno responseneverneverno response>8never0001241833150000.00113944311051001002
7No Urgent PlaceAloneSunny556PMCoffee House2hMale46-49Married partner1Graduate degree (Masters or Doctorate)Management$87500 - $99999no responsenever<1<1never<100112184893749.51016874212212000002
8No Urgent PlaceAloneSunny8010AMCoffee House1dFemale31-35Married partner0Some college - no degreeComputer & Mathematical$100000 or Moreno responseneverneverno response>8never1011241033150000.00113942311051001002
9No Urgent PlaceFriend(s)Rainy5510PMBar2hFemale31-35Married partner0Some college - no degreeComputer & Mathematical$100000 or Moreno responseneverneverno response>8never101022233150000.00023945211051001002